Advertisement

Topic Classification on Short Reflective Writings for Monitoring Students’ Progress

  • Leonard K. M. PoonEmail author
  • Zichao Li
  • Gary Cheng
Conference paper
  • 2.3k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10309)

Abstract

Reflection has been widely considered as an important element in student learning in higher education. Among different forms of reflective writing, one-minute papers can quickly and easily get students to reflect on their learning. Unlike short quizzes, the responses to one-minute papers could cover a wide open range and could require more time to review and summarize. When one-minute papers are administrated online, their responses are available in electronic form and this facilitates a computational approach for analysis. In this paper, we propose a machine learning approach to analyzing the students’ responses to one-minute papers. We build a text classifier to identify the topics discussed in the responses. Our results of a preliminary study conducted in a blended learning course demonstrate that the classifier can effectively detect the topics and the proposed method can be used to monitor student progress based on the detected topics.

Keywords

Topic classification One-minute papers Reflective writings Blended learning Learning analytics 

References

  1. 1.
    Anderson, D., Burns, S.: One-minute paper: student perception of learning gains. Coll. Stud. J. 47(1), 219–227 (2013)Google Scholar
  2. 2.
    Angelo, T.A., Cross, K.P.: Classroom Assessment Techniques: A Handbook for College Teachers. Jossey-Bass, San Francisco (1993)Google Scholar
  3. 3.
    Ashakiran, S., Deepthi, R.: One-minute paper: a thinking centered assessment tool. Int. J. Med. Update 8(2), 1–9 (2013)Google Scholar
  4. 4.
    Barber, D.: Bayesian reasoning and machine learning. Cambridge University Press, Cambridge (2012)zbMATHGoogle Scholar
  5. 5.
    Blei, D.M.: Probabilistic topic models. Commun. ACM 55(4), 77–84 (2012)CrossRefGoogle Scholar
  6. 6.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  7. 7.
    Burrows, S., Gurevych, I., Stein, B.: The eras and trends of automatic short answer grading. Int. J. Artif. Intell. Educ. 25(1), 60–117 (2015)CrossRefGoogle Scholar
  8. 8.
    Carless, D., Zhou, J.: Starting small in assessment change: short in-class written responses. Assess. Eval. High. Educ. 41(7), 1114–1127 (2016)CrossRefGoogle Scholar
  9. 9.
    Chen, Y., Yu, B., Zhang, X., Yu, Y.: Topic modeling for evaluating students’ reflective writing: a case study of pre-service teachers’ journals. In: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, pp. 1–5 (2016)Google Scholar
  10. 10.
    Cowan, J.: Noteworthy matters for attention in reflective journal writing. Act. Learn. High. Educ. 15(1), 53–64 (2014)CrossRefGoogle Scholar
  11. 11.
    Dos Santos, J.C.A., Favero, E.L.: Practical use of a latent semantic analysis (LSA) model for automatic evaluation of written answers. J. Braz. Comput. Soc. 21(1), 21 (2015)CrossRefGoogle Scholar
  12. 12.
    Farrell, T.S.C.: Does writing promote reflective practice? In: Renandya, W.A., Widodo, H.P. (eds.) English Language Teaching Today. ELE, vol. 5, pp. 83–94. Springer, Cham (2016). doi: 10.1007/978-3-319-38834-2_7 CrossRefGoogle Scholar
  13. 13.
    Fink, L.D.: Creating Significant Learning Experiences: An Integrated Approach to Designing College Courses. Jossey-Bass, San Francisco (2003)Google Scholar
  14. 14.
    Gibson, A., Kitto, K.: Analysing reflective text for learning analytics: an approach using anomaly recontextualisation. In: Proceedings of the Fifth International Conference on Learning Analytics and Knowledge, pp. 275–279 (2015)Google Scholar
  15. 15.
    Halverson, J.: Minute paper and interdisciplinary studies: pre-test/post-test study. J. High. Educ. Theory Pract. 14(3), 44 (2014)Google Scholar
  16. 16.
    Hatton, N., Smith, D.: Reflection in teacher education: towards definition and implementation. Teach. Teach. Educ. 11(1), 33–49 (1995)CrossRefGoogle Scholar
  17. 17.
    Kwan, F.: Formative assessment: the one-minute paper vs. the daily quiz. J. Instr. Pedagogies 5, 1–8 (2011)Google Scholar
  18. 18.
    Landauer, T.K., Foltz, P.W., Laham, D.: An introduction to latent semantic analysis. Discourse Process. 25(2–3), 259–284 (1998)CrossRefGoogle Scholar
  19. 19.
    Lengelle, R., Meijers, F., Poell, R., Post, M.: The effects of creative, expressive, and reflective writing on career learning: an explorative study. J. Vocat. Behav. 83(3), 419–427 (2013)CrossRefGoogle Scholar
  20. 20.
    Lucas, G.M.: Initiating student-teacher contact via personalized responses to one-minute papers. Coll. Teach. 58(2), 39–42 (2010)CrossRefGoogle Scholar
  21. 21.
    Mittal, H., Syamala Devi, M.: Computerized evaluation of subjective answers using hybrid technique. In: Saini, H.S., Sayal, R., Rawat, S.S. (eds.) Innovations in Computer Science and Engineering. AISC, vol. 413, pp. 295–303. Springer, Singapore (2016). doi: 10.1007/978-981-10-0419-3_35 CrossRefGoogle Scholar
  22. 22.
    Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 311–318 (2002)Google Scholar
  23. 23.
    Rogers, R.R.: Reflection in higher education: a concept analysis. Innov. High. Educ. 26(1), 37–57 (2001)CrossRefGoogle Scholar
  24. 24.
    Ryan, M., Ryan, M.: Theorising a model for teaching and assessing reflective learning in higher education. High. Educ. Res. Dev. 32(2), 244–257 (2013)CrossRefGoogle Scholar
  25. 25.
    Shum, S.B., Sándor, Á., Goldsmith, R., Wang, X., Bass, R., McWilliams, M.: Reflecting on reflective writing analytics: assessment challenges and iterative evaluation of a prototype tool. In: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, pp. 213–222 (2016)Google Scholar
  26. 26.
    Stead, D.R.: A review of the one-minute paper. Act. Learn. High. Educ. 6(2), 118–131 (2005)CrossRefGoogle Scholar
  27. 27.
    Ullmann, T.D.: Keywords of written reflection – a comparison between reflective and descriptive datasets. In: Proceedings of the 5th Workshop on Awareness and Reflection in Technology Enhanced Learning, vol. 1465, pp. 83–96 (2015)Google Scholar
  28. 28.
    Ullmann, T.D., Wild, F., Scott, P.: Comparing automatically detected reflective texts with human judgements. In: Proceedings of the 2nd Workshop on Awareness and Reflection in Technology-Enhanced Learning, pp. 101–116 (2012)Google Scholar
  29. 29.
    Vonderwell, S.: Assessing online learning and teaching: Adapting the minute paper. TechTrends 48(4), 29–31 (2004)CrossRefGoogle Scholar
  30. 30.
    Vonderwell, S.K., Boboc, M.: Promoting formative assessment in online teaching and learning. TechTrends 57(4), 22–27 (2013)CrossRefGoogle Scholar
  31. 31.
    Wade, R.C., Yarbrough, D.B.: Portfolios: A tool for reflective thinking in teacher education? Teach. Teach. Educ. 12(1), 63–79 (1996)CrossRefGoogle Scholar
  32. 32.
    Whittard, D.: Reflections on the one-minute paper. Int. Rev. Econ. Educ. 20, 1–12 (2015)CrossRefGoogle Scholar
  33. 33.
    Wolff, M., Wagner, M.J., Poznanski, S., Schiller, J., Santen, S.: Not another boring lecture: Engaging learners with active learning techniques. J. Emerg. Med. 48(1), 85–93 (2015)CrossRefGoogle Scholar
  34. 34.
    Wolstenholme, J.: Evidence based practice using formative assessment in library research support. Evid. Libr. Inf. Pract. 10(3), 4–29 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Mathematics and Information TechnologyThe Education University of Hong KongHong Kong SARChina
  2. 2.Department of Computer Science and EngineeringThe Chinese University of Hong KongHong Kong SARChina

Personalised recommendations